Stealing Deep Reinforcement Learning Models for Fun and Profit

9 June 2020

Yang Liu

Papers citing "Stealing Deep Reinforcement Learning Models for Fun and Profit"

43 / 43 papers shown

Title
Position: A taxonomy for reporting and describing AI security incidents L. Bieringer Kevin Paeth Andreas Wespi Kathrin Grosse Alexandre Alahi Kathrin Grosse 130 0 0 19 Dec 2024
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning Jianwen Sun Tianwei Zhang Xiaofei Xie Lei Ma Yan Zheng Kangjie Chen Yang Liu AAML 46 115 0 14 May 2020
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty Xiyue Zhang Xiaofei Xie Lei Ma Xiaoning Du Q. Hu Yang Liu Jianjun Zhao Meng Sun AAML 42 76 0 24 Apr 2020
Cryptanalytic Extraction of Neural Network Models Nicholas Carlini Matthew Jagielski Ilya Mironov FedML MLAU MIACV AAML 110 135 0 10 Mar 2020
High Accuracy and High Fidelity Extraction of Neural Networks Matthew Jagielski Nicholas Carlini David Berthelot Alexey Kurakin Nicolas Papernot MLAU MIACV 81 377 0 03 Sep 2019
Optimal Attacks on Reinforcement Learning Policies Alessio Russo Alexandre Proutiere AAML 41 42 0 31 Jul 2019
Characterizing Attacks on Deep Reinforcement Learning Xinlei Pan Chaowei Xiao Warren He Shuang Yang Jian Peng ... Jinfeng Yi Zijiang Yang Mingyan D. Liu Yue Liu D. Song AAML 51 70 0 21 Jul 2019
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies Vahid Behzadan W. Hsu OffRL 26 19 0 03 Jun 2019
A framework for the extraction of Deep Neural Networks by leveraging public data Soham Pal Yash Gupta Aditya Shukla Aditya Kanade S. Shevade V. Ganapathy FedML MLAU MIACV 63 56 0 22 May 2019
Stealing Neural Networks via Timing Side Channels Vasisht Duddu D. Samanta D. V. Rao V. Balas AAML MLAU FedML 63 134 0 31 Dec 2018
Knockoff Nets: Stealing Functionality of Black-Box Models Tribhuvanesh Orekondy Bernt Schiele Mario Fritz MLAU 86 534 0 06 Dec 2018
Exploring Connections Between Active Learning and Model Extraction Varun Chandrasekaran Kamalika Chaudhuri Irene Giacomelli Shane Walker Songbai Yan MIACV 178 158 0 05 Nov 2018
Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks Sanghyun Hong Michael Davinroy Yigitcan Kaya S. Locke Ian Rackow Kevin Kulda Dana Dachman-Soled Tudor Dumitras MIACV 53 89 0 08 Oct 2018
Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures Mengjia Yan Christopher W. Fletcher Josep Torrellas MIACV FedML 61 247 0 14 Aug 2018
Model Reconstruction from Model Explanations S. Milli Ludwig Schmidt Anca Dragan Moritz Hardt FAtt 52 177 0 13 Jul 2018
Learning to Drive in a Day Alex Kendall Jeffrey Hawke David Janz Przemyslaw Mazur Daniele Reda John M. Allen Vinh-Dieu Lam Alex Bewley Amar Shah 95 656 0 01 Jul 2018
Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data Jacson Rodrigues Correia-Silva Rodrigo Berriel C. Badue Alberto F. de Souza Thiago Oliveira-Santos MLAU 82 175 0 14 Jun 2018
Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations Taesung Lee Ben Edwards Ian Molloy D. Su AAML 60 41 0 31 May 2018
PRADA: Protecting against DNN Model Stealing Attacks Mika Juuti S. Szyller Samuel Marchal Nadarajah Asokan SILM AAML 66 442 0 07 May 2018
Improving Transferability of Adversarial Examples with Input Diversity Cihang Xie Zhishuai Zhang Yuyin Zhou Song Bai Jianyu Wang Zhou Ren Alan Yuille AAML 97 1,116 0 19 Mar 2018
Stealing Hyperparameters in Machine Learning Binghui Wang Neil Zhenqiang Gong AAML 134 464 0 14 Feb 2018
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring Yossi Adi Carsten Baum Moustapha Cissé Benny Pinkas Joseph Keshet 61 674 0 13 Feb 2018
Model Extraction Warning in MLaaS Paradigm M. Kesarwani B. Mukhoty Vijay Arya S. Mehta MLAU 44 140 0 20 Nov 2017
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation Yuhuai Wu Elman Mansimov Shun Liao Roger C. Grosse Jimmy Ba OffRL 52 626 0 17 Aug 2017
Proximal Policy Optimization Algorithms John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov OffRL 444 18,931 0 20 Jul 2017
MagNet: a Two-Pronged Defense against Adversarial Examples Dongyu Meng Hao Chen AAML 46 1,206 0 25 May 2017
Adversarial Attacks on Neural Network Policies Sandy Huang Nicolas Papernot Ian Goodfellow Yan Duan Pieter Abbeel MLAU AAML 83 837 0 08 Feb 2017
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks Vahid Behzadan Arslan Munir AAML SILM 63 276 0 16 Jan 2017
Embedding Watermarks into Deep Neural Networks Yusuke Uchida Yuki Nagai S. Sakazawa Shiníchi Satoh 109 606 0 15 Jan 2017
Sample Efficient Actor-Critic with Experience Replay Ziyun Wang V. Bapst N. Heess Volodymyr Mnih Rémi Munos Koray Kavukcuoglu Nando de Freitas 97 761 0 03 Nov 2016
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 230 4,103 0 18 Oct 2016
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving Shai Shalev-Shwartz Shaked Shammah Amnon Shashua 96 833 0 11 Oct 2016
Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning Yuke Zhu Roozbeh Mottaghi Eric Kolve Joseph J. Lim Abhinav Gupta Li Fei-Fei Ali Farhadi VGen 61 1,521 0 16 Sep 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 102 1,803 0 09 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 239 8,548 0 16 Aug 2016
Generative Adversarial Imitation Learning Jonathan Ho Stefano Ermon GAN 131 3,101 0 10 Jun 2016
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization Chelsea Finn Sergey Levine Pieter Abbeel 108 946 0 01 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.1K 16,931 0 16 Feb 2016
Asynchronous Methods for Deep Reinforcement Learning Volodymyr Mnih Adria Puigdomenech Badia M. Berk Mirza Alex Graves Timothy Lillicrap Tim Harley David Silver Koray Kavukcuoglu 189 8,833 0 04 Feb 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 90 3,955 0 24 Nov 2015
Continuous control with deep reinforcement learning Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel N. Heess Tom Erez Yuval Tassa David Silver Daan Wierstra 310 13,214 0 09 Sep 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 243 19,017 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 249 14,893 1 21 Dec 2013